Towards the development of new subtype-specific muscarinic receptor radiopharmaceuticals — Radiosynthesis and ex vivo biodistribution of [<sup>18</sup>F]3-(4-(2-(2-(2-fluoroethoxy)ethoxy)ethylthio)-1,2,5-thiadiazol-3-yl)-1-methyl-1,2,5,6-tetrahydropyridine
Bibliographic record
Abstract
Muscarinic receptors have been implicated in neurological disorders including Alzheimer’s disease, Parkinson’s disease, and schizophrenia. Nineteen derivatives of thiadiazolyltetrahydropyridine (TZTP), a core that has previously shown high affinities towards muscarinic receptor subtypes, were synthesized and evaluated via in vitro binding assays. The title compound, a fluoro-polyethyleneglycol analog of TZTP (4c), was subsequently labelled with fluorine-18. Fluorine-18-labelled 4c was produced, via an automated synthesis, in an average radiochemical yield of 36% (uncorrected for decay), with high radiochemical purity (>99%) and high specific activity (326 GBq/µmol; end-of-bombardment), within 40 min (n = 3). Ex vivo biodistribution studies following tail-vein injection of [18F]4c in conscious rats displayed sufficient brain uptake (0.4%–0.7% injected dose / gram of wet tissue in all brain regions at 5 min post injection); however, there were substantial polar metabolites present in the brain, thereby precluding future use of [18F]4c for imaging in the central nervous system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".